MBZUAI Assistant Professor Samuel Horváth is researching federated learning to address the tension between data privacy and the predictive power of machine learning models. Federated learning trains models on decentralized data, keeping sensitive information on devices. Horváth's research focuses on designing algorithms that can efficiently train on distributed data while respecting user privacy. Why it matters: This work is crucial for advancing AI in sensitive domains like healthcare, where privacy regulations limit centralized data collection.
Technology Innovation Institute (TII) in Abu Dhabi has launched the UAE’s first secure cloud technologies programme via its Cryptography Research Center (CRC). The program will focus on advancing Privacy Enhancing Technologies (PETs) like fully homomorphic encryption (FHE) and secure multi-party computation (MPC). TII researchers are also developing hardware accelerators to improve the efficiency of FHE. Why it matters: The initiative addresses growing security and privacy challenges in cloud computing, positioning the UAE as a leader in advanced cryptographic solutions for data protection.
Patrick van der Smagt, Director of AI Research at Volkswagen Group, discussed the use of generative machine learning models for predicting and controlling complex stochastic systems in robotics. The talk highlighted examples in robotics and beyond and addressed the challenges of achieving quality and trust in AI systems. He also mentioned his involvement in a European industry initiative on trust in AI and his membership in the AI Council of the State of Bavaria. Why it matters: Understanding control in robotics, along with trust in AI, are key issues for further development of autonomous systems, especially in industrial applications within the GCC region.
A new paper from MBZUAI researchers explores using ChatGPT to combat the spread of fake news. The researchers, including Preslav Nakov and Liangming Pan, demonstrate that ChatGPT can be used to fact-check published information. Their paper, "Fact-Checking Complex Claims with Program-Guided Reasoning," was accepted at ACL 2023. Why it matters: This research highlights the potential of large language models to address the growing challenge of misinformation, with implications for maintaining information integrity in the digital age.
This article mentions KAUST in the context of the 251st American Chemical Society National Meeting. However, it contains no specific details about AI or related research activities. The content is primarily a copyright notice for King Abdullah University of Science and Technology. Why it matters: This mention provides minimal information about KAUST's involvement in the event and lacks substantial AI-related content.
TII's Cryptography Research Center (CRC) has formed partnerships with several international universities, including Ruhr-University Bochum, Radboud University, Khalifa University, and others, to advance cryptography research. The collaborations cover areas like privacy-preserving cloud computing, lightweight cryptography, enhanced IoT protocols, and post-quantum cryptography schemes. CRC had previously partnered with Yale University and co-authored a book with New York University. Why it matters: These partnerships signal the UAE's commitment to developing advanced cryptographic capabilities and contributing to global research in data security and privacy.
A new framework for constructing confidence sets for causal orderings within structural equation models (SEMs) is presented. It leverages a residual bootstrap procedure to test the goodness-of-fit of causal orderings, quantifying uncertainty in causal discovery. The method is computationally efficient and suitable for medium-sized problems while maintaining theoretical guarantees as the number of variables increases. Why it matters: This offers a new dimension of uncertainty quantification that enhances the robustness and reliability of causal inference in complex systems, but there is no indication of connection to the Middle East.
Researchers from KAUST, University of St. Andrews, and the Center for Unconventional Processes of Sciences have developed an uncrackable security system using optical chips. The system uses silicon chips with complex structures that are irreversibly changed to send information, achieving "perfect secrecy" through a one-time key. This method leverages classical physics and the second law of thermodynamics to ensure that keys are never stored, communicated, or recreated, making interception impossible. Why it matters: This breakthrough has the potential to revolutionize communications privacy globally, offering an unbreakable method for securing confidential data on public channels.